A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload

Authors

Melissa K Knox
Paras D Mehta
Lynette E Dorsey
Christine Yang
Laura A Petersen

Publication Date

1-1-2023

Journal

ACI - Applied Clinical Informatics

DOI

10.1055/a-1993-7627

PMID

36473498

PMCID

PMC9891851

PubMedCentral® Posted Date

2-1-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Workload, Personnel Staffing and Scheduling, Nursing Staff, Hospital, Pharmaceutical Preparations, Electronic Data Processing, Workforce, nursing care, bar code medication administration, assessment, registered nurse, licensed practical nurse, workload

Abstract

OBJECTIVE: The aim of the study is to introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for the examination of nurse staffing and workload using data generated during regular nursing workflow.

METHODS: Using 1 year (October 1, 2014-September 30, 2015) of BCMA data for 11 acute care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within 2 hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start time.

RESULTS: As the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional

CONCLUSION: Medication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.